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The course of the disease in female patients with metastatic mammary carcinoma can vary greatly. In this connection, the individual prognosis depends on a complex interaction of tumor- and patient-related factors. To take account of such differences, it is necessary to employ individual methods of treatment which are suited to the course of each patient's disease. Prof. Possinger and Dr. Schmid (Charite Berlin) and Prof. Wischnewsky (University of Bremen) have developed an approach that can help to achieve this goal with the aid of computerized machine learning techniques (MLT).

The use of machine learning methods can be beneficial in oncology in two respects. On the one hand, an attempt can be made to individually estimate clinically relevant parameters like, for example, the recurrence probability or expected survival time as precisely as possible based on the established prognostic factors. And on the other hand, it may be possible with the aid of MLT to understand structural relationships between the clinical result and measured or established tumor-/patient-related variables.

To analyze the possible benefits of machine learning techniques for patients with metastatic breast cancer, the aim of study FEM-D-2 is to investigate whether it is possible to characterize those patients who either do or do not respond to a specific treatment with a precision of 90%, prospectively estimate the time until worsening of the disease under a given treatment, and classify patients in groups with favorable and poor chances of medium-term survival.

The use of inductive learning algorithms with machine learning also makes it possible to very accurately estimate the time until progression of the tumor growth. In patients who respond to letrozole therapy, the time until tumor progression depends on factors like pain, age, body mass index, disease-free interval, main localization of metastatic spread, and response to previous estrogen therapy. Only very minimal differences are found when comparing the actual time until progression of the disease and that calculated by the system (at least for survival times < 1 year). Furthermore, using machine learning techniques it has become possible to use initial data assessed before a letrozole treatment to estimate the survival time and distinguish patients with a high risk of dying soon from other patients with a more favorable prognosis.

Comparison of the the individual pretherapeutic predictions from the computer or doctor with the patient data obtained in a first- or second-line treatment of metastatic breast cancer after progression of the disease

Secondary Outcome Measures:

Determination of the individual response at 3 monthly assessments

Enrollment:

13

Study Start Date:

April 2002

Primary Completion Date:

March 2005 (Final data collection date for primary outcome measure)

Eligibility

Information from the National Library of Medicine

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Ages Eligible for Study:

18 Years and older (Adult, Senior)

Sexes Eligible for Study:

Female

Accepts Healthy Volunteers:

No

Criteria

Inclusion criteria for the second-line therapy:

Patients can only take part in the study if they meet all of the following criteria:

Patients with a primary or recurrent local advanced mammary carcinoma which cannot be curatively treated with surgical procedures or radiation therapy, or patients with a metastatic mammary carcinoma after antiestrogen pretreatment.

Patients with a recurrence under adjuvant antiestrogen therapy (e.g. tamoxifen; with or without adjuvant chemotherapy) which was administered for at least 6 months or a recurrence within the last 12 months after adjuvant antiestrogen therapy (e.g. tamoxifen, with or without adjuvant chemotherapy) which was administered for at least 6 months or progression under palliative first-line antiestrogen therapy can be included.

At most, a previous palliative cytostatic treatment is possible

Measurable or assessable metastases in at least one organ system with objective proof of progression; that is, evidence of newly occurring lesions or an increase in size of preexisting lesions by more than 25% with measurable metastases or worsening with assessable changes within the last 3 months before inclusion in the study

In the case of bone metastases, imaging methods should verify that at least one preexisting osteolysis or the lytic part of an assessable mixed lesion has increased in size, or that new measurable or assessable bone metastases have developed. In assessable mixed lesions, the measurable part must constitute at least 50% of the size of the metastasis.

If no previous images are available, the increase in bone pain in connection with the detectable, measurable osteolyses or assessable mixed metastases in the pretreatment image are regarded as progression.

Previous radiotherapy is permitted as long as the irradiated area is not the only measurable lesion

Estimated life expectancy of at least 12 weeks

Performance status of 50 or higher on the Karnofsky scale or WHO grade 0-2.

Age ≥18 years

Written informed consent of the patient

Exclusion criteria for the second-line therapy:

Patients are not allowed to take part in the study if they meet at least one of the following criteria:

Cerebral metastasis

Lymphangitis carcinomatosa of the lung (> 50% of the lung affected)

Very extensive liver metastasis (in ultrasound or CT > 33% of the liver replaced by metastases)

Inflammatory mammary carcinoma

Other primary malignant diseases (except in situ carcinoma of the cervix or adequately treated basal cell carcinoma of the skin)